HW 03

1 - Du Bois challenge.

income <- read.csv("data/income.csv")

income <- income |>
    mutate(
        Average_Income = as.integer(Average_Income),
        ClassLabel = factor(
            paste0(Class, " | $", 
                   format(Average_Income, 
                          big.mark = ",")
                  ),
            levels = unique(paste0(Class, " | $", 
                               format(Average_Income, 
                                      big.mark = ",")))
            )
    ) |>

    pivot_longer(
        cols = Rent:Other, ## list slice-like syntax to get the ordered columns
        names_to = "Category", 
        values_to = "Percent"
    ) |>

    mutate(
        Category = factor(Category, 
                          levels = c("Other", 
                                     "Tax", 
                                     "Clothes", 
                                     "Food", 
                                     "Rent")),
        text_color = ifelse(Category == "Rent", 
                            "white", 
                            "black")
    ) |>

    group_by(ClassLabel) |>
    mutate(pos = cumsum(Percent) - Percent / 2) |>
    ungroup()

category_colors <- c(
    Rent = "black",
    Food = "slateblue4",
    Clothes = "rosybrown2",
    Tax = "gray60",
    Other = "tan"
)

glimpse(income)

Rows: 35
Columns: 7
$ Class          <chr> "$100-200", "$100-200", "$100-200", "$10…
$ Average_Income <int> 139, 139, 139, 139, 139, 249, 249, 249, …
$ ClassLabel     <fct> "$100-200 | $  139", "$100-200 | $  139"…
$ Category       <fct> Rent, Food, Clothes, Tax, Other, Rent, F…
$ Percent        <dbl> 19.0, 43.0, 28.0, 9.9, 0.1, 22.0, 47.0, …
$ text_color     <chr> "white", "black", "black", "black", "bla…
$ pos            <dbl> 9.50, 40.50, 76.00, 94.95, 99.95, 11.00,…

du_bois <- ggplot(
    income, 
    aes(x = fct_rev(ClassLabel), 
        y = Percent, 
        fill = Category)
    ) +

    geom_col(color = "black", 
             width = 0.7) +

    geom_text(data = filter(income, 
                            Percent > 1),
              aes(label = paste0(round(Percent, 1), "%"), 
                  y = pos, 
                  color = text_color), 
              size = 2.5
    ) +
    scale_fill_manual(values = category_colors) +
    scale_color_manual(values = c("white" = "white", 
                                  "black" = "black")) +

    coord_flip() +
    scale_y_continuous(labels = NULL) +
  
    annotate("text", 
             x = c(1, 2.5, 4.5, 6.5), 
             y = 102, 
             label = c("Well-To-Do", 
                       "Comfortable", 
                       "Fair", 
                       "Poor"), 
             size = 2.5, 
             angle = 90) +
  
  labs(
      x = NULL, 
      y = NULL, 
      title = "Annual Expenditure For Provided Data",
      text_color = NULL
  ) +

  theme(
      axis.title = element_blank(),
      axis.text.y = element_text(face = "bold", 
                                 size = 8),
      panel.grid = element_blank(),
      legend.title = element_blank(),
      legend.position = "top",
      plot.title = element_text(hjust = 0.5, margin = margin(b = 10))
  ) +

  guides(fill = guide_legend(reverse = TRUE), 
         color = "none")

ggbackground(du_bois, "images/paper.jpg")

2 - COVID survey - interpret

3 - COVID survey - reconstruct

4 - COVID survey - re-reconstruct

5 - COVID survey - another view